Predicting Titanic survival using linear regression involves building a model that uses features like age, gender, class, etc., to predict the likelihood of passengers surviving the Titanic disaster. Linear regression assumes a linear relationship between these features and the probability of survival, aiming to find coefficients that best fit the data. The model predicts survival probabilities based on historical data, aiming to classify passengers into survived or not survived categories based on their feature values.
Movie rating prediction using linear regression involves creating a model that uses various features (such as genre, director, cast, etc.) to predict the numerical rating (like IMDb score) of a movie. The model assumes a linear relationship between these features and the rating, aiming to find coefficients that minimize the difference between predicted and actual ratings. It's used to forecast how well a movie might be received based on its attributes, helping in decision-making for producers, distributors, and viewers.
Sales prediction using linear regression entails developing a model that forecasts future sales based on historical data and relevant influencing factors. By analyzing past sales alongside variables like marketing spend, economic conditions, and seasonality, linear regression identifies patterns and establishes a linear relationship between these factors and sales outcomes. This method facilitates informed business decisions by providing estimates of potential sales volumes, aiding in budgeting, resource allocation, and strategic planning.